Lecture Videos

Leif Sörnmo: Bioelectrical Signal Processing in Cardiac and Neurological Applications

Recorded at Helsinki, February 2008.

1. Introduction   3. The Brain   11. Evoked Potentials   24. The Electrical Activity of the Heart

 1 Introduction
Bioelectrical Signal Processing
The Human Body
The Biomedical Signal: Reflections of a Secret
Origin of Bioelectrical Signals
Why this Textbook?
Where is Bioelectricity Measured?
The Brain, Heart and Muscles and their Electrical Activity
Electrical Signals - Spontaneous
Electrical Signals - Stimulation
Multimodal Signal Recording
 2 Purposes of Biomedical Signal Processing
Signal Processing Constrains
Noisy Signal Situation - Stress Testing
 3 The Biomedical Signal Processing Challenge
Why Simulated Signals?

The Brain
Neurons = Nerve Cells
Inside the Neuron
Cerebral Cortex and its Lobes
The Cerebral Cortex: Some Basic Facts
The Nervous System
Electroencephalogram - EEG
EEG - (Un)synchronized Activity
EEG acquisition
Important EEG Rhythms
EEG Signals - Examples
Alpha, Beta and Blink Artifacts
The Use of EEG Today
Onset of Epileptic Seizure

 4 Brain Computer Interface
BCI Principle
Brain Computer Interface
EEG Modeling Aspects
Noise and Artifacts
EMG in the EEG
Eye Movements and the EEG
"Optimal" Noise Rejection
"Eye Movement" Electrodes
Noise Rejection by Weighting
 5 Linear Weighting
Weights and the MSE
MSE Minimization
Assumption of Stationarity
Noise Rejection by Weighting
Assumption of Stationarity
Correction of ElectroOculoGram
Adaptive Noise Rejection
MSE Criterion Minimization
Noise Rejection - Filtered Reference Signals
 6 Spectral Analysis of the EEG
Fourier-Based Spectral Analysis
The Periodogram
Properties of the Periodogram
Periodogram and Segmentation
Spectral Parameters
Trending of Spectral Parameters
EEG and AR Modeling
 7 Autoregressive (AR) Modeling
AR Modeling and Linear Prediction
AR Parameter Estimation
The Normal Equation
Multivariate AR Models
EEG Activity Propagation after Finger Movement
AR Modeling and Sampling Rate
 8 AR Modeling and Sampling Rate
Adaptive EEG Segmentation
Segmentation - Sliding Window
Criterion for EEG Segmentation
Spectra of a Segmented Signal
Spectral Analysis of Nonstationary Signals
Criterion for EEG Segmentation
Spectral Analysis of Nonstationary Signals
Short-Time Fourier Transformation
Photic Stimulation at Different Rates
Heart Surgery of an Infant
 9 STFT and Beyond
Time-Varying AR Model
Time-Varying AR during Seizure
WWD-based Time-Frequency Analysis
The Ambiquity Function
Ambiquity Function, cont't
The Analytical Signal
Analytical Signal in Math Terms
10 Ambiguity Function, cont'
Wigner-Ville Distribution (WVD)
Comparison of STFT and WVD
The Pseudo WVD
Cross-Term Reduction
CWD and 2-Component Signal
11 Evoked Potentials
EP = A Transient Waveform
Examples of Evoked Potentials
EP - Definitions
Auditive Evoked Potentials - AEPs
Visual Evoked Potentials - VEPs
Somatosensory Evoked Potentials - SEPs
SEPs during Spinal Surgery
EP Scalp Distribution
Brainstem Auditive EP in Newborns
BAEPs of Healthy Children
12 Cognitive EPs
Ensemble Formation
Formation of an EP Ensemble
10 Superimposed EPs
Model for Ensemble Averaging
Noise Assumptions
13 Ensemble Averaging
Noise Variance
Noise Assumptions
Reduction of Noise Level
Exponential Averaging
14 Introduction
Noise Reduction of EPs with Varying Noise Level
Weighted Averaging
15 Weighted Averaging, cont'
Weighted Averaging: An Example
Robust Waveform Averaging
The Effect of Latency Variations
Lowpass Filtering of the Signal
Latency Variation and Lowpass Filtering
Techniques for Correction of Latency Variations
Estimation of Latency
Woody's Method
Woody's Method: Different SNRs
Noise Reduction by Filtering
Wiener Eiltering
Filtering of Evoked Potentials
Limitations of Wiener Filtering
16 Problem Solving
17 Problem Solving
18 Tracking of EP Morphology
Selection of Basis Functions
Orthogonal Expansions
Basis Functions: An Example
Calculation of the Weights
Mean-Square Weight Estimation
19 Truncated Expansion
Examples of Basis Functions
Sine/Cosine Modeling
MSE Basis Functions
Karhunen-Loeve Basis Functions
KL Performance Index
20 Karhunen-Loeve Basis Functions
How to get Rx?
Example: KL Basis Functions
Time-Varying Filter Interpretation
Modeling with Damped Sinusoids
Adaptive Estimation of Weights
Estimation Using Sine/Cosine
Estimation Using KL Functions
21 Wavelet Analysis
Wavelet Applications
The Correlation Operation
the Mother Wavelet
The Wavelet Transform
The Scalogram
The Discrete Wavelet Transform
Multiresolution Analysis
22 Multiresolution Analysis, cont'
Multiresolution Analysis Exemplified
The Scaling Function
The Approximation Signal x0(t)
Multiresolution Analysis
The Approximation Signal xj(t)
The Multiresolution Property
23 The Wavelet Function
The Wavelet Series Expansion
Multiresolution Signal Analysis: A Classical Example
The Refinement Equation
The Haar Scaling Function
Maar Multiresolution Analysis
Haar Scaling and Wavelet Functions
Computational Coefficients
Filter Bank Implementation
DWT Calculation
Inverse DWT Calculation
Coifflet Multiresolution Analysis
Scaling Coefficient in Noise
The Wavelet Series Expansion
Denoising of Evoked Potentials
EP Wavelet Analysis
24 The Electrical Activity of the Heart
The Heart ...
Blood Flow od the Heart
Conduction System of the Heart
Cardiac Excitation
Electrical Vectors of the Heart
Cardiac Excitation
The Cardiac Cycle & Wave Shape
Extremity Leads - I, II, III
Årecordial Leads - V1 to V6
The Standard 12-Lead ECG
25 ECG Waves: P-QRS-T
Normal Sinus Rhythms
Heart Rate Variability
Arrhythmias: Ectopic Beats
Arrhythmias: Bi & Trigenimy
Arrhythmias: Atrial Flutter/Fibrillation
Arrhythmias: Ventricular Flutter/Fibrillation
26 Heart Attack
Myocardial Ischemia
Noise in the ECG
Clinical ECG Applications
The Exercise Stress Test
Stress Testing and Ischemia - ECG Reaction 1
Stress Testing and Ischemia - ECG Reaction 2
ST Reaction Versus Heart Rate - Decision Regions
High-Resolution ECG and Cardiac Late Potentials
Spectral Analysis of the ECG?
Spectral Analysis of the Heart Rate?
EEG, EP, and ECG: Time Base?
ECG Signal Processing
ECG Filtering Techniques ...
ECG Baseline Wander
27 Discussion
28 Baseline Filtering: Phase Aspects
Baseline Filtering: An Example
Cubic Spline Interpolation
50/60 Hz LTI Notch Filter
Nonlinear 50-Hz Filtering
Nonlinear Filtering Exemplified
50/60 Hz Filtering
29 50/60 Hz Filtering
QRS Detection Problems
Spectral Detection Problems
QRS Detection
Models for QRS Detection
Design of Linear Detection Filter
Simple Detector Filter Structures
Simple Filters for QRS Detection - Frequency Response
Design of Nonlinear Transformation
Envelope-based Detection
Envelope Examples
Preprocessor Output
QRS Detection: Decision Rule
QRS Detector Performance
30 QRS End Delineation
LPD-based Delineation
Data Compression
ECG Data Redundancy
Data Compression of ECG Signals
Lossless Data Compression based on Linear Prediction
Linear Prediction
Lossy Data Compression
Example of AZTEC
The SAPA Principle
Example of SAPA
KLT-based Data Compression
KLT Compression with Tolerance
KLT using Universal Data
KLT using Subject-Specific Data
Handling Interbeat Redundancy
Performance Measures
31 What's behind the Beat?
Heart Rate Variability (HRV)
Conductor's Heart Rate
Heart Rate and Respiration
RR Interval Series
24-hour RR Interval
24h RR Interval Histograms
The RR Interval Series - the Tachogram
Heart Rhythm Representations
Lowpass Filtered Event Series
32 Integral Pulse Frequency Modulation (IPFM) Model
Output of the IPFM Model
Heart Timing Signal
Heart Timing Signal: Example
Why Spectral Analysis of HRV?
Spectrum of Counts
Lomb's Periodogram
Lomb's Periodogram and the Classical Periodogram
HRV Spectral Analysis
HRV Spectrum: A Comparison
Power Spectrum with One Modulation Frequency (0.16 Hz)
Power Spectrum with Two Modulation Frequencies
HRV and Sudden Cardiac Death
33 Ectopic Beat Correction
Do not maltreat the signals ...